Setup
Set working
directory
knitr::opts_chunk$set(warning = FALSE, message = FALSE)
knitr::opts_knit$set(root.dir = normalizePath("../data"))
Packages
pacman::p_load(psych, lme4, nlme, tidyverse, lmerTest, gridExtra, ggplot2,patidyverse,tidyr, nlme, lmerTest, ggplot2, ggthemes, dplyr, rio, na.omit, performance, olsrr, tidyr, psych, dplyr,rowSum ,scapeMCMC, MCMCglmm, agridat, mlmRev, car, jtools, ggridges, DescTools, stringr, scater, gridExtra, cowplot, writexl, dplyr, tidyverse, foreign, irr, magrittr, plyr)
Data
# Load data
data_fragestellung2 <- rio::import("N183_multipom_r_rready.sav")
# select variables and define categorical data as factors
df2 <- data_fragestellung2 %>%
mutate(the_sex = as.factor(the_sex),
pat_sex = as.factor(pat_sex),
pat_id = as.factor(pat_id),
the_id = as.factor(the_id),
bedingung = as.factor(bedingung)) %>%
select(BAI_t1, BAI_t2, BAI_t3, BAI_t4, BAI_t5, BAI_t6,
BSI_t1, BSI_t2, BSI_t3, BSI_t4, BSI_t5, BSI_t6,
BDI_t1, BDI_t2, BDI_t3, BDI_t4, BDI_t5, BDI_t6,
n_sessions, age_th, age_pat_startth,
pom_pre, pom_int1, pom_int2, pom_post, pom_fu1, pom_fu2, pat_id, the_id, pat_sex, the_sex, EFT_all, SR_all, DB_all, I_all, B_all, C_all, PC_all, PD_all, CF_all, PE_all)
Datenbeschreibung
Deskriptive
Analysen
describe(df2)
## vars n mean sd median trimmed mad min max range
## BAI_t1 1 61 15.16 8.47 14.00 14.80 7.41 0.00 40.00 40.00
## BAI_t2 2 56 11.14 8.27 8.50 10.35 6.67 0.00 34.00 34.00
## BAI_t3 3 51 9.51 7.48 8.00 8.63 5.93 0.00 34.00 34.00
## BAI_t4 4 58 6.60 7.25 5.00 5.35 4.45 0.00 33.00 33.00
## BAI_t5 5 50 7.72 8.73 5.00 6.15 5.93 0.00 40.00 40.00
## BAI_t6 6 43 5.65 6.25 4.00 4.46 4.45 0.00 26.00 26.00
## BSI_t1 7 61 47.74 22.91 52.00 48.00 28.17 4.00 86.00 82.00
## BSI_t2 8 56 33.34 21.15 31.50 31.30 22.98 1.00 91.00 90.00
## BSI_t3 9 51 29.41 17.61 27.00 28.32 19.27 1.00 86.00 85.00
## BSI_t4 10 58 23.72 20.87 17.50 21.29 18.53 1.00 101.00 100.00
## BSI_t5 11 50 23.26 19.93 19.50 20.45 19.27 0.00 93.00 93.00
## BSI_t6 12 44 19.20 14.41 18.50 17.92 14.08 1.00 63.00 62.00
## BDI_t1 13 61 20.49 10.71 19.00 20.08 13.34 1.00 43.00 42.00
## BDI_t2 14 56 14.07 10.07 13.00 13.20 11.86 0.00 38.00 38.00
## BDI_t3 15 51 12.04 7.85 11.00 11.24 7.41 0.00 34.00 34.00
## BDI_t4 16 58 8.86 7.83 6.50 8.08 7.41 0.00 35.00 35.00
## BDI_t5 17 50 8.44 7.96 8.00 7.25 7.41 0.00 38.00 38.00
## BDI_t6 18 44 6.89 6.73 5.50 6.14 7.41 0.00 26.00 26.00
## n_sessions 19 61 24.82 5.47 26.00 25.63 2.97 9.00 31.00 22.00
## age_th 20 60 33.18 6.23 32.00 32.25 2.97 23.00 51.00 28.00
## age_pat_startth 21 61 30.30 10.61 27.00 28.33 5.93 20.00 68.00 48.00
## pom_pre 22 61 73.28 33.56 77.33 74.07 43.98 5.00 126.33 121.33
## pom_int1 23 56 51.12 31.58 49.83 48.40 36.08 3.00 138.33 135.33
## pom_int2 24 51 44.62 25.88 40.00 43.00 28.66 1.00 127.33 126.33
## pom_post 25 58 34.79 29.68 25.83 31.48 25.70 1.00 145.00 144.00
## pom_fu1 26 50 34.27 29.84 27.50 30.08 26.93 0.00 144.33 144.33
## pom_fu2 27 43 27.67 21.00 26.67 25.79 23.23 1.00 77.67 76.67
## pat_id* 28 61 31.00 17.75 31.00 31.00 22.24 1.00 61.00 60.00
## the_id* 29 60 16.32 8.69 16.00 16.35 10.38 1.00 32.00 31.00
## pat_sex* 30 60 1.57 0.50 2.00 1.58 0.00 1.00 2.00 1.00
## the_sex* 31 61 1.77 0.42 2.00 1.84 0.00 1.00 2.00 1.00
## EFT_all 32 61 2.10 0.42 2.15 2.09 0.45 1.30 3.13 1.83
## SR_all 33 61 1.55 0.31 1.53 1.53 0.33 1.00 2.42 1.42
## DB_all 34 61 1.87 0.39 1.81 1.85 0.44 1.17 2.83 1.67
## I_all 35 61 1.68 0.56 1.58 1.62 0.62 1.00 3.50 2.50
## B_all 36 61 2.11 0.29 2.09 2.09 0.26 1.42 2.93 1.51
## C_all 37 61 2.32 0.34 2.29 2.30 0.31 1.73 3.15 1.42
## PC_all 38 61 4.24 0.43 4.15 4.24 0.55 3.39 5.00 1.61
## PD_all 39 61 1.38 0.24 1.33 1.35 0.20 1.07 2.22 1.16
## CF_all 40 61 4.57 0.26 4.58 4.58 0.29 4.00 5.00 1.00
## PE_all 41 61 2.06 0.44 2.07 2.03 0.51 1.33 3.14 1.80
## skew kurtosis se
## BAI_t1 0.38 -0.05 1.08
## BAI_t2 0.86 -0.22 1.10
## BAI_t3 1.14 1.16 1.05
## BAI_t4 1.89 3.75 0.95
## BAI_t5 1.89 3.82 1.23
## BAI_t6 1.74 2.78 0.95
## BSI_t1 -0.11 -1.15 2.93
## BSI_t2 0.85 0.18 2.83
## BSI_t3 0.74 0.51 2.47
## BSI_t4 1.27 1.74 2.74
## BSI_t5 1.32 1.80 2.82
## BSI_t6 0.79 0.26 2.17
## BDI_t1 0.22 -0.90 1.37
## BDI_t2 0.59 -0.48 1.35
## BDI_t3 0.82 0.23 1.10
## BDI_t4 0.94 0.50 1.03
## BDI_t5 1.40 2.38 1.13
## BDI_t6 0.81 -0.21 1.01
## n_sessions -1.34 1.04 0.70
## age_th 1.40 1.55 0.80
## age_pat_startth 1.72 2.57 1.36
## pom_pre -0.16 -1.12 4.30
## pom_int1 0.72 0.03 4.22
## pom_int2 0.76 0.65 3.62
## pom_post 1.23 1.65 3.90
## pom_fu1 1.43 2.40 4.22
## pom_fu2 0.57 -0.59 3.20
## pat_id* 0.00 -1.26 2.27
## the_id* 0.02 -1.07 1.12
## pat_sex* -0.26 -1.96 0.06
## the_sex* -1.25 -0.43 0.05
## EFT_all 0.15 -0.81 0.05
## SR_all 0.50 -0.07 0.04
## DB_all 0.39 -0.63 0.05
## I_all 0.99 0.86 0.07
## B_all 0.64 0.68 0.04
## C_all 0.58 -0.23 0.04
## PC_all 0.07 -1.04 0.05
## PD_all 1.24 1.44 0.03
## CF_all -0.14 -1.08 0.03
## PE_all 0.45 -0.58 0.06
describeBy(df2, group = df2$pat_sex)
##
## Descriptive statistics by group
## group: 1
## vars n mean sd median trimmed mad min max range
## BAI_t1 1 26 15.46 6.72 16.50 15.36 5.19 2.00 32.00 30.00
## BAI_t2 2 24 9.96 7.18 7.50 9.40 7.41 1.00 26.00 25.00
## BAI_t3 3 21 7.90 6.48 6.00 7.24 4.45 0.00 24.00 24.00
## BAI_t4 4 23 5.30 6.42 4.00 4.11 5.93 0.00 27.00 27.00
## BAI_t5 5 18 6.89 7.31 3.00 6.31 4.45 0.00 23.00 23.00
## BAI_t6 6 15 2.80 3.14 2.00 2.46 2.97 0.00 10.00 10.00
## BSI_t1 7 26 47.42 23.44 51.50 47.36 29.65 12.00 84.00 72.00
## BSI_t2 8 24 30.21 17.57 30.50 28.80 20.76 5.00 70.00 65.00
## BSI_t3 9 21 25.57 16.54 24.00 23.82 14.83 4.00 67.00 63.00
## BSI_t4 10 23 21.96 23.62 15.00 18.00 14.83 1.00 101.00 100.00
## BSI_t5 11 18 24.06 19.60 20.00 22.25 20.02 2.00 75.00 73.00
## BSI_t6 12 16 13.06 10.27 10.50 12.71 11.86 1.00 30.00 29.00
## BDI_t1 13 26 20.04 11.24 18.50 19.23 14.08 6.00 43.00 37.00
## BDI_t2 14 24 11.79 9.01 11.50 11.00 8.90 0.00 38.00 38.00
## BDI_t3 15 21 10.86 7.66 8.00 10.12 5.93 0.00 34.00 34.00
## BDI_t4 16 23 7.52 8.58 5.00 6.05 5.93 0.00 35.00 35.00
## BDI_t5 17 18 8.06 7.16 7.50 7.44 7.41 0.00 26.00 26.00
## BDI_t6 18 16 4.50 5.22 2.00 4.14 2.97 0.00 14.00 14.00
## n_sessions 19 26 25.38 3.81 25.50 25.77 3.71 14.00 31.00 17.00
## age_th 20 26 33.77 5.84 32.00 33.14 2.97 26.00 51.00 25.00
## age_pat_startth 21 26 33.35 12.52 29.50 31.86 8.15 20.00 68.00 48.00
## pom_pre 22 26 72.62 34.07 77.17 72.26 46.70 21.00 126.33 105.33
## pom_int1 23 24 45.32 26.65 43.83 43.78 28.66 6.67 101.67 95.00
## pom_int2 24 21 39.06 24.91 37.67 36.25 21.74 4.00 107.00 103.00
## pom_post 25 23 31.25 33.57 19.33 25.58 20.26 1.00 145.00 144.00
## pom_fu1 26 18 34.41 28.19 28.17 31.77 31.63 3.00 108.00 105.00
## pom_fu2 27 15 17.07 14.49 12.00 16.18 14.83 2.00 43.67 41.67
## pat_id* 28 26 27.12 17.93 24.50 26.77 22.24 1.00 58.00 57.00
## the_id* 29 26 16.38 9.87 17.50 16.41 13.34 1.00 32.00 31.00
## pat_sex* 30 26 1.00 0.00 1.00 1.00 0.00 1.00 1.00 0.00
## the_sex* 31 26 1.62 0.50 2.00 1.64 0.00 1.00 2.00 1.00
## EFT_all 32 26 2.06 0.44 2.11 2.07 0.60 1.30 2.64 1.33
## SR_all 33 26 1.50 0.27 1.48 1.49 0.28 1.04 2.12 1.08
## DB_all 34 26 1.76 0.35 1.74 1.75 0.37 1.17 2.44 1.28
## I_all 35 26 1.64 0.59 1.47 1.57 0.56 1.00 3.50 2.50
## B_all 36 26 2.06 0.22 2.06 2.05 0.26 1.78 2.58 0.80
## C_all 37 26 2.28 0.32 2.23 2.25 0.25 1.77 3.06 1.29
## PC_all 38 26 4.28 0.42 4.26 4.29 0.47 3.44 5.00 1.56
## PD_all 39 26 1.37 0.22 1.33 1.36 0.20 1.07 1.80 0.73
## CF_all 40 26 4.49 0.26 4.46 4.49 0.29 4.00 4.92 0.92
## PE_all 41 26 2.04 0.45 1.98 2.03 0.54 1.33 2.89 1.56
## skew kurtosis se
## BAI_t1 0.10 0.10 1.32
## BAI_t2 0.61 -0.88 1.47
## BAI_t3 0.91 -0.10 1.41
## BAI_t4 1.81 3.33 1.34
## BAI_t5 0.99 -0.46 1.72
## BAI_t6 0.94 -0.36 0.81
## BSI_t1 -0.09 -1.51 4.60
## BSI_t2 0.58 -0.59 3.59
## BSI_t3 0.89 -0.10 3.61
## BSI_t4 1.78 3.03 4.93
## BSI_t5 0.89 0.09 4.62
## BSI_t6 0.33 -1.61 2.57
## BDI_t1 0.48 -0.99 2.20
## BDI_t2 0.93 0.68 1.84
## BDI_t3 1.17 1.44 1.67
## BDI_t4 1.64 2.32 1.79
## BDI_t5 0.73 -0.14 1.69
## BDI_t6 0.65 -1.29 1.30
## n_sessions -1.22 1.58 0.75
## age_th 1.36 1.41 1.15
## age_pat_startth 1.18 0.42 2.46
## pom_pre -0.02 -1.44 6.68
## pom_int1 0.46 -0.94 5.44
## pom_int2 1.01 0.47 5.44
## pom_post 1.83 3.28 7.00
## pom_fu1 0.83 0.07 6.65
## pom_fu2 0.51 -1.38 3.74
## pat_id* 0.20 -1.41 3.52
## the_id* 0.04 -1.37 1.94
## pat_sex* NaN NaN 0.00
## the_sex* -0.45 -1.87 0.10
## EFT_all -0.11 -1.67 0.09
## SR_all 0.42 -0.48 0.05
## DB_all 0.33 -0.82 0.07
## I_all 1.20 1.51 0.12
## B_all 0.42 -0.80 0.04
## C_all 0.72 -0.04 0.06
## PC_all -0.01 -1.05 0.08
## PD_all 0.39 -0.97 0.04
## CF_all 0.19 -1.12 0.05
## PE_all 0.22 -1.37 0.09
## ------------------------------------------------------------
## group: 2
## vars n mean sd median trimmed mad min max range
## BAI_t1 1 34 15.24 9.64 13.00 14.79 12.60 0.00 40.00 40.00
## BAI_t2 2 31 12.29 9.03 9.00 11.40 7.41 0.00 34.00 34.00
## BAI_t3 3 29 10.86 8.07 9.00 10.08 7.41 0.00 34.00 34.00
## BAI_t4 4 34 7.50 7.83 5.50 6.18 5.19 0.00 33.00 33.00
## BAI_t5 5 31 8.35 9.63 6.00 6.48 5.93 0.00 40.00 40.00
## BAI_t6 6 27 7.19 7.11 5.00 6.22 4.45 0.00 26.00 26.00
## BSI_t1 7 34 48.79 22.67 52.50 49.43 26.69 4.00 86.00 82.00
## BSI_t2 8 31 35.94 23.83 33.00 33.68 25.20 1.00 91.00 90.00
## BSI_t3 9 29 32.41 18.34 29.00 31.64 20.76 1.00 86.00 85.00
## BSI_t4 10 34 24.71 19.38 21.50 23.29 23.72 1.00 77.00 76.00
## BSI_t5 11 31 23.19 20.64 20.00 20.20 20.76 0.00 93.00 93.00
## BSI_t6 12 27 21.89 15.05 23.00 20.96 14.83 1.00 63.00 62.00
## BDI_t1 13 34 21.06 10.53 22.00 21.14 11.86 1.00 42.00 41.00
## BDI_t2 14 31 15.58 10.73 14.00 14.96 13.34 0.00 36.00 36.00
## BDI_t3 15 29 12.79 8.13 11.00 12.32 8.90 0.00 30.00 30.00
## BDI_t4 16 34 9.68 7.38 9.50 9.32 8.90 0.00 24.00 24.00
## BDI_t5 17 31 8.90 8.51 8.00 7.48 7.41 0.00 38.00 38.00
## BDI_t6 18 27 7.78 6.87 6.00 7.22 7.41 0.00 26.00 26.00
## n_sessions 19 34 24.35 6.55 26.50 25.18 3.71 9.00 31.00 22.00
## age_th 20 33 32.82 6.64 31.00 31.89 2.97 23.00 51.00 28.00
## age_pat_startth 21 34 27.79 8.44 25.00 26.25 4.45 20.00 62.00 42.00
## pom_pre 22 34 74.93 33.47 77.50 76.83 42.01 5.00 123.67 118.67
## pom_int1 23 31 55.61 35.15 51.33 52.29 38.55 3.00 138.33 135.33
## pom_int2 24 29 48.83 26.65 44.33 47.92 26.19 1.00 127.33 126.33
## pom_post 25 34 36.88 27.46 31.33 35.02 30.64 1.00 104.33 103.33
## pom_fu1 26 31 34.88 31.44 27.67 30.11 23.72 0.00 144.33 144.33
## pom_fu2 27 27 32.06 21.23 33.33 31.06 19.27 1.00 77.67 76.67
## pat_id* 28 34 33.88 17.57 32.50 34.29 22.24 3.00 61.00 58.00
## the_id* 29 33 16.27 7.94 16.00 16.30 7.41 2.00 30.00 28.00
## pat_sex* 30 34 2.00 0.00 2.00 2.00 0.00 2.00 2.00 0.00
## the_sex* 31 34 1.88 0.33 2.00 1.96 0.00 1.00 2.00 1.00
## EFT_all 32 34 2.12 0.42 2.11 2.10 0.34 1.39 3.13 1.74
## SR_all 33 34 1.58 0.35 1.54 1.57 0.37 1.00 2.42 1.42
## DB_all 34 34 1.96 0.41 1.86 1.95 0.45 1.28 2.83 1.56
## I_all 35 34 1.72 0.55 1.65 1.68 0.51 1.00 3.25 2.25
## B_all 36 34 2.16 0.34 2.17 2.14 0.29 1.42 2.93 1.51
## C_all 37 34 2.36 0.36 2.32 2.35 0.36 1.73 3.15 1.42
## PC_all 38 34 4.20 0.44 4.11 4.19 0.58 3.39 5.00 1.61
## PD_all 39 34 1.38 0.27 1.28 1.34 0.12 1.13 2.22 1.09
## CF_all 40 34 4.64 0.24 4.68 4.66 0.27 4.17 5.00 0.83
## PE_all 41 34 2.06 0.43 2.07 2.02 0.42 1.33 3.14 1.80
## skew kurtosis se
## BAI_t1 0.42 -0.46 1.65
## BAI_t2 0.78 -0.56 1.62
## BAI_t3 1.06 0.83 1.50
## BAI_t4 1.76 3.03 1.34
## BAI_t5 1.91 3.49 1.73
## BAI_t6 1.36 0.95 1.37
## BSI_t1 -0.17 -0.88 3.89
## BSI_t2 0.74 -0.33 4.28
## BSI_t3 0.56 0.57 3.41
## BSI_t4 0.64 -0.51 3.32
## BSI_t5 1.43 2.21 3.71
## BSI_t6 0.71 0.10 2.90
## BDI_t1 -0.06 -0.91 1.81
## BDI_t2 0.37 -1.00 1.93
## BDI_t3 0.58 -0.54 1.51
## BDI_t4 0.32 -1.15 1.27
## BDI_t5 1.51 2.51 1.53
## BDI_t6 0.73 -0.30 1.32
## n_sessions -1.07 -0.16 1.12
## age_th 1.38 1.32 1.16
## age_pat_startth 2.27 5.90 1.45
## pom_pre -0.32 -0.88 5.74
## pom_int1 0.63 -0.35 6.31
## pom_int2 0.55 0.63 4.95
## pom_post 0.53 -0.79 4.71
## pom_fu1 1.57 2.78 5.65
## pom_fu2 0.35 -0.77 4.09
## pat_id* -0.13 -1.22 3.01
## the_id* -0.03 -1.04 1.38
## pat_sex* NaN NaN 0.00
## the_sex* -2.27 3.25 0.06
## EFT_all 0.47 -0.24 0.07
## SR_all 0.37 -0.39 0.06
## DB_all 0.26 -0.83 0.07
## I_all 0.75 0.07 0.09
## B_all 0.42 0.05 0.06
## C_all 0.40 -0.52 0.06
## PC_all 0.19 -1.11 0.08
## PD_all 1.49 1.65 0.05
## CF_all -0.43 -0.85 0.04
## PE_all 0.71 0.06 0.07
describeBy(df2, group = df2$the_sex)
##
## Descriptive statistics by group
## group: 1
## vars n mean sd median trimmed mad min max range
## BAI_t1 1 14 14.00 7.03 16.50 14.17 4.45 2.00 24.00 22.00
## BAI_t2 2 14 9.14 7.68 6.50 8.42 5.19 1.00 26.00 25.00
## BAI_t3 3 11 7.55 4.87 9.00 7.00 2.97 2.00 18.00 16.00
## BAI_t4 4 13 5.08 4.82 5.00 4.45 4.45 0.00 17.00 17.00
## BAI_t5 5 10 6.00 6.78 3.00 4.88 1.48 0.00 21.00 21.00
## BAI_t6 6 10 3.90 3.25 2.50 3.62 2.97 0.00 10.00 10.00
## BSI_t1 7 14 47.86 28.36 37.00 47.67 34.10 12.00 86.00 74.00
## BSI_t2 8 14 28.14 19.41 22.00 26.58 17.79 5.00 70.00 65.00
## BSI_t3 9 11 25.82 18.22 26.00 23.56 17.79 5.00 67.00 62.00
## BSI_t4 10 13 24.69 18.50 21.00 23.64 17.79 1.00 60.00 59.00
## BSI_t5 11 10 25.30 24.20 15.50 22.25 19.27 0.00 75.00 75.00
## BSI_t6 12 10 13.80 8.78 14.50 13.50 11.86 2.00 28.00 26.00
## BDI_t1 13 14 20.43 12.32 18.50 19.58 14.08 8.00 43.00 35.00
## BDI_t2 14 14 11.93 10.48 9.50 10.75 7.41 0.00 38.00 38.00
## BDI_t3 15 11 10.64 8.50 7.00 8.67 1.48 5.00 34.00 29.00
## BDI_t4 16 13 9.38 7.11 7.00 9.00 4.45 0.00 23.00 23.00
## BDI_t5 17 10 8.10 8.69 7.00 6.88 9.64 0.00 26.00 26.00
## BDI_t6 18 10 3.60 4.86 1.50 2.75 2.22 0.00 14.00 14.00
## n_sessions 19 14 24.57 4.88 25.50 24.92 2.22 14.00 31.00 17.00
## age_th 20 14 34.14 6.19 33.00 33.67 3.71 23.00 51.00 28.00
## age_pat_startth 21 14 32.79 14.65 27.50 30.83 7.41 21.00 68.00 47.00
## pom_pre 22 14 72.95 41.36 61.67 72.92 56.83 21.00 125.33 104.33
## pom_int1 23 14 43.12 30.25 31.17 41.28 28.17 6.67 101.67 95.00
## pom_int2 24 11 38.97 27.47 37.67 34.30 21.74 13.00 107.00 94.00
## pom_post 25 13 35.77 26.16 31.00 34.48 23.72 1.00 84.67 83.67
## pom_fu1 26 10 35.40 34.87 23.33 30.75 29.65 0.00 108.00 108.00
## pom_fu2 27 10 18.70 13.82 17.00 17.67 15.32 2.00 43.67 41.67
## pat_id* 28 14 29.43 20.04 37.00 29.25 28.91 1.00 60.00 59.00
## the_id* 29 14 17.00 9.88 19.00 16.92 14.83 3.00 32.00 29.00
## pat_sex* 30 14 1.29 0.47 1.00 1.25 0.00 1.00 2.00 1.00
## the_sex* 31 14 1.00 0.00 1.00 1.00 0.00 1.00 1.00 0.00
## EFT_all 32 14 1.94 0.40 1.89 1.90 0.44 1.48 2.86 1.37
## SR_all 33 14 1.47 0.23 1.47 1.47 0.28 1.04 1.83 0.79
## DB_all 34 14 1.75 0.32 1.76 1.74 0.31 1.28 2.39 1.11
## I_all 35 14 1.71 0.50 1.65 1.67 0.56 1.17 2.75 1.58
## B_all 36 14 2.06 0.17 2.02 2.05 0.20 1.78 2.42 0.64
## C_all 37 14 2.28 0.32 2.26 2.27 0.25 1.77 2.94 1.17
## PC_all 38 14 4.07 0.51 3.96 4.05 0.44 3.39 5.00 1.61
## PD_all 39 14 1.42 0.34 1.28 1.39 0.17 1.07 2.22 1.16
## CF_all 40 14 4.51 0.27 4.46 4.50 0.31 4.17 5.00 0.83
## PE_all 41 14 1.92 0.39 1.84 1.88 0.33 1.44 2.86 1.42
## skew kurtosis se
## BAI_t1 -0.51 -1.21 1.88
## BAI_t2 0.87 -0.63 2.05
## BAI_t3 0.54 -0.59 1.47
## BAI_t4 0.90 0.26 1.34
## BAI_t5 1.17 -0.20 2.14
## BAI_t6 0.59 -1.17 1.03
## BSI_t1 0.11 -1.89 7.58
## BSI_t2 0.70 -0.76 5.19
## BSI_t3 0.84 -0.23 5.49
## BSI_t4 0.56 -1.10 5.13
## BSI_t5 0.80 -0.80 7.65
## BSI_t6 0.14 -1.55 2.78
## BDI_t1 0.54 -1.14 3.29
## BDI_t2 1.06 0.25 2.80
## BDI_t3 1.83 2.25 2.56
## BDI_t4 0.42 -1.14 1.97
## BDI_t5 0.70 -0.80 2.75
## BDI_t6 1.03 -0.47 1.54
## n_sessions -1.21 0.42 1.30
## age_th 1.04 1.78 1.65
## age_pat_startth 1.25 0.14 3.91
## pom_pre 0.10 -1.89 11.05
## pom_int1 0.56 -1.18 8.08
## pom_int2 1.21 0.65 8.28
## pom_post 0.57 -1.14 7.26
## pom_fu1 0.82 -0.72 11.03
## pom_fu2 0.44 -1.32 4.37
## pat_id* -0.08 -1.69 5.36
## the_id* -0.03 -1.51 2.64
## pat_sex* 0.85 -1.36 0.13
## the_sex* NaN NaN 0.00
## EFT_all 0.68 -0.46 0.11
## SR_all -0.07 -1.11 0.06
## DB_all 0.28 -0.93 0.09
## I_all 0.71 -0.85 0.13
## B_all 0.30 -0.56 0.05
## C_all 0.39 -0.81 0.09
## PC_all 0.51 -1.09 0.14
## PD_all 1.23 0.25 0.09
## CF_all 0.36 -1.33 0.07
## PE_all 0.90 0.08 0.10
## ------------------------------------------------------------
## group: 2
## vars n mean sd median trimmed mad min max range
## BAI_t1 1 47 15.51 8.89 13.00 15.10 8.90 0.00 40.00 40.00
## BAI_t2 2 42 11.81 8.43 9.00 11.00 6.67 0.00 34.00 34.00
## BAI_t3 3 40 10.05 8.02 8.00 9.12 6.67 0.00 34.00 34.00
## BAI_t4 4 45 7.04 7.80 5.00 5.62 4.45 0.00 33.00 33.00
## BAI_t5 5 40 8.15 9.18 6.00 6.47 6.67 0.00 40.00 40.00
## BAI_t6 6 33 6.18 6.86 4.00 4.93 4.45 0.00 26.00 26.00
## BSI_t1 7 47 47.70 21.39 52.00 48.13 23.72 4.00 86.00 82.00
## BSI_t2 8 42 35.07 21.65 32.00 32.94 22.98 1.00 91.00 90.00
## BSI_t3 9 40 30.40 17.54 27.00 29.59 19.27 1.00 86.00 85.00
## BSI_t4 10 45 23.44 21.70 16.00 20.70 16.31 1.00 101.00 100.00
## BSI_t5 11 40 22.75 19.04 20.00 20.25 18.53 1.00 93.00 92.00
## BSI_t6 12 34 20.79 15.43 22.00 19.71 17.79 1.00 63.00 62.00
## BDI_t1 13 47 20.51 10.33 20.00 20.46 11.86 1.00 42.00 41.00
## BDI_t2 14 42 14.79 9.96 14.00 14.06 11.86 0.00 36.00 36.00
## BDI_t3 15 40 12.43 7.73 11.50 11.91 8.15 0.00 30.00 30.00
## BDI_t4 16 45 8.71 8.09 6.00 7.81 7.41 0.00 35.00 35.00
## BDI_t5 17 40 8.53 7.89 8.50 7.28 6.67 0.00 38.00 38.00
## BDI_t6 18 34 7.85 6.95 7.00 7.21 8.15 0.00 26.00 26.00
## n_sessions 19 47 24.89 5.68 26.00 25.69 2.97 9.00 31.00 22.00
## age_th 20 46 32.89 6.28 31.00 32.00 2.97 25.00 51.00 26.00
## age_pat_startth 21 47 29.55 9.16 27.00 28.21 5.93 20.00 62.00 42.00
## pom_pre 22 47 73.38 31.40 77.33 74.50 36.08 5.00 126.33 121.33
## pom_int1 23 42 53.79 31.91 50.67 50.84 29.16 3.00 138.33 135.33
## pom_int2 24 40 46.17 25.57 41.67 45.36 26.69 1.00 127.33 126.33
## pom_post 25 45 34.50 30.89 24.33 30.60 27.18 1.00 145.00 144.00
## pom_fu1 26 40 33.99 28.94 30.33 30.25 27.92 1.00 144.33 143.33
## pom_fu2 27 33 30.39 22.18 32.67 28.94 30.64 1.00 77.67 76.67
## pat_id* 28 47 31.47 17.22 30.00 31.46 20.76 2.00 61.00 59.00
## the_id* 29 46 16.11 8.40 15.50 16.24 8.15 1.00 30.00 29.00
## pat_sex* 30 46 1.65 0.48 2.00 1.68 0.00 1.00 2.00 1.00
## the_sex* 31 47 2.00 0.00 2.00 2.00 0.00 2.00 2.00 0.00
## EFT_all 32 47 2.15 0.42 2.21 2.15 0.45 1.30 3.13 1.83
## SR_all 33 47 1.57 0.33 1.54 1.56 0.31 1.00 2.42 1.42
## DB_all 34 47 1.90 0.41 1.83 1.88 0.49 1.17 2.83 1.67
## I_all 35 47 1.67 0.59 1.58 1.60 0.62 1.00 3.50 2.50
## B_all 36 47 2.13 0.32 2.18 2.11 0.33 1.42 2.93 1.51
## C_all 37 47 2.33 0.35 2.31 2.31 0.34 1.73 3.15 1.42
## PC_all 38 47 4.29 0.39 4.30 4.29 0.39 3.67 5.00 1.33
## PD_all 39 47 1.37 0.21 1.33 1.35 0.20 1.07 1.87 0.80
## CF_all 40 47 4.59 0.25 4.67 4.61 0.31 4.00 5.00 1.00
## PE_all 41 47 2.10 0.45 2.11 2.08 0.55 1.33 3.14 1.80
## skew kurtosis se
## BAI_t1 0.46 -0.19 1.30
## BAI_t2 0.81 -0.29 1.30
## BAI_t3 1.03 0.63 1.27
## BAI_t4 1.78 2.96 1.16
## BAI_t5 1.84 3.48 1.45
## BAI_t6 1.53 1.65 1.19
## BSI_t1 -0.26 -0.85 3.12
## BSI_t2 0.84 0.15 3.34
## BSI_t3 0.70 0.62 2.77
## BSI_t4 1.37 2.00 3.23
## BSI_t5 1.44 2.71 3.01
## BSI_t6 0.62 -0.21 2.65
## BDI_t1 0.05 -0.97 1.51
## BDI_t2 0.42 -0.71 1.54
## BDI_t3 0.45 -0.42 1.22
## BDI_t4 1.02 0.67 1.21
## BDI_t5 1.57 3.15 1.25
## BDI_t6 0.65 -0.48 1.19
## n_sessions -1.34 0.98 0.83
## age_th 1.49 1.41 0.93
## age_pat_startth 1.59 2.41 1.34
## pom_pre -0.31 -0.80 4.58
## pom_int1 0.74 0.14 4.92
## pom_int2 0.60 0.66 4.04
## pom_post 1.32 1.87 4.60
## pom_fu1 1.59 3.43 4.58
## pom_fu2 0.39 -0.89 3.86
## pat_id* 0.06 -1.21 2.51
## the_id* 0.02 -1.00 1.24
## pat_sex* -0.62 -1.65 0.07
## the_sex* NaN NaN 0.00
## EFT_all -0.02 -0.76 0.06
## SR_all 0.44 -0.33 0.05
## DB_all 0.32 -0.78 0.06
## I_all 1.03 0.99 0.09
## B_all 0.51 0.16 0.05
## C_all 0.60 -0.26 0.05
## PC_all 0.03 -1.11 0.06
## PD_all 0.67 -0.60 0.03
## CF_all -0.30 -0.92 0.04
## PE_all 0.30 -0.70 0.06
describeBy(df2, group = data_fragestellung2$bedingung)
##
## Descriptive statistics by group
## group: 1
## vars n mean sd median trimmed mad min max range
## BAI_t1 1 28 14.54 8.30 13.00 13.67 6.67 3.00 40.00 37.00
## BAI_t2 2 26 11.23 7.21 9.00 10.64 5.93 1.00 29.00 28.00
## BAI_t3 3 23 10.13 7.29 9.00 9.53 7.41 0.00 34.00 34.00
## BAI_t4 4 26 6.38 5.43 5.00 5.95 5.93 0.00 18.00 18.00
## BAI_t5 5 22 7.45 9.14 4.50 5.83 5.19 0.00 40.00 40.00
## BAI_t6 6 22 4.23 3.91 4.00 3.89 5.93 0.00 13.00 13.00
## BSI_t1 7 28 43.25 21.42 43.00 42.71 22.98 5.00 86.00 81.00
## BSI_t2 8 26 35.23 22.44 31.50 33.64 23.72 5.00 90.00 85.00
## BSI_t3 9 23 30.83 19.88 27.00 28.68 13.34 1.00 86.00 85.00
## BSI_t4 10 26 25.85 21.17 18.00 24.23 21.50 1.00 77.00 76.00
## BSI_t5 11 22 22.59 21.20 17.50 19.39 17.05 1.00 93.00 92.00
## BSI_t6 12 22 18.50 13.43 19.50 17.56 15.57 1.00 45.00 44.00
## BDI_t1 13 28 19.96 11.01 18.50 19.67 11.86 1.00 43.00 42.00
## BDI_t2 14 26 15.46 10.03 14.50 14.95 8.90 0.00 38.00 38.00
## BDI_t3 15 23 13.26 9.08 11.00 12.53 7.41 0.00 34.00 34.00
## BDI_t4 16 26 9.65 8.05 10.00 9.36 10.38 0.00 23.00 23.00
## BDI_t5 17 22 8.64 9.12 7.00 7.06 8.90 0.00 38.00 38.00
## BDI_t6 18 22 8.18 6.66 8.50 7.83 8.90 0.00 21.00 21.00
## n_sessions 19 28 24.89 4.88 26.00 25.46 2.97 11.00 31.00 20.00
## age_th 20 28 34.36 5.72 33.00 33.75 4.45 26.00 51.00 25.00
## age_pat_startth 21 28 31.39 10.66 27.50 30.04 5.93 20.00 62.00 42.00
## pom_pre 22 28 68.06 32.54 66.17 67.61 43.00 7.33 125.33 118.00
## pom_int1 23 26 54.44 32.75 51.33 52.21 35.58 6.67 131.33 124.67
## pom_int2 24 23 47.46 29.67 39.33 44.12 22.73 1.00 127.33 126.33
## pom_post 25 26 37.63 29.95 30.17 35.79 34.59 1.00 104.33 103.33
## pom_fu1 26 22 33.71 32.37 25.17 29.20 25.45 1.00 144.33 143.33
## pom_fu2 27 22 28.09 20.20 30.00 27.09 23.97 1.00 68.33 67.33
## pat_id* 28 28 20.54 13.89 17.50 19.62 16.31 2.00 61.00 59.00
## the_id* 29 27 11.85 7.82 11.00 11.17 7.41 1.00 32.00 31.00
## pat_sex* 30 27 1.52 0.51 2.00 1.52 0.00 1.00 2.00 1.00
## the_sex* 31 28 1.79 0.42 2.00 1.83 0.00 1.00 2.00 1.00
## EFT_all 32 28 2.28 0.41 2.36 2.28 0.40 1.48 3.13 1.65
## SR_all 33 28 1.52 0.28 1.53 1.52 0.34 1.00 2.12 1.12
## DB_all 34 28 1.78 0.40 1.77 1.76 0.49 1.17 2.67 1.50
## I_all 35 28 1.58 0.43 1.57 1.57 0.43 1.00 2.42 1.42
## B_all 36 28 2.07 0.28 2.01 2.05 0.26 1.60 2.87 1.27
## C_all 37 28 2.28 0.33 2.26 2.25 0.24 1.77 3.15 1.38
## PC_all 38 28 4.24 0.40 4.15 4.24 0.36 3.44 5.00 1.56
## PD_all 39 28 1.41 0.26 1.33 1.38 0.20 1.07 2.22 1.16
## CF_all 40 28 4.56 0.29 4.48 4.57 0.38 4.00 4.94 0.95
## PE_all 41 28 2.25 0.44 2.22 2.25 0.42 1.39 3.14 1.75
## skew kurtosis se
## BAI_t1 1.13 1.43 1.57
## BAI_t2 0.78 -0.34 1.41
## BAI_t3 1.26 2.56 1.52
## BAI_t4 0.65 -0.67 1.06
## BAI_t5 2.07 4.61 1.95
## BAI_t6 0.42 -0.98 0.83
## BSI_t1 0.19 -1.05 4.05
## BSI_t2 0.64 -0.46 4.40
## BSI_t3 1.07 0.65 4.14
## BSI_t4 0.58 -0.82 4.15
## BSI_t5 1.65 2.92 4.52
## BSI_t6 0.40 -1.02 2.86
## BDI_t1 0.38 -0.96 2.08
## BDI_t2 0.46 -0.48 1.97
## BDI_t3 0.76 -0.34 1.89
## BDI_t4 0.21 -1.47 1.58
## BDI_t5 1.62 2.61 1.94
## BDI_t6 0.23 -1.26 1.42
## n_sessions -1.44 1.37 0.92
## age_th 1.23 0.91 1.08
## age_pat_startth 1.29 0.90 2.01
## pom_pre 0.11 -1.22 6.15
## pom_int1 0.54 -0.45 6.42
## pom_int2 1.00 0.51 6.19
## pom_post 0.45 -1.09 5.87
## pom_fu1 1.76 3.50 6.90
## pom_fu2 0.22 -1.17 4.31
## pat_id* 0.74 0.30 2.63
## the_id* 0.70 0.14 1.51
## pat_sex* -0.07 -2.07 0.10
## the_sex* -1.32 -0.27 0.08
## EFT_all -0.07 -0.64 0.08
## SR_all 0.08 -0.80 0.05
## DB_all 0.49 -0.76 0.08
## I_all 0.30 -1.03 0.08
## B_all 0.85 0.26 0.05
## C_all 0.95 0.63 0.06
## PC_all 0.17 -0.67 0.08
## PD_all 1.37 1.59 0.05
## CF_all -0.07 -1.40 0.05
## PE_all 0.12 -0.64 0.08
## ------------------------------------------------------------
## group: 2
## vars n mean sd median trimmed mad min max range
## BAI_t1 1 33 15.70 8.70 17.00 15.81 7.41 0.00 32.00 32.00
## BAI_t2 2 30 11.07 9.21 7.50 10.04 8.15 0.00 34.00 34.00
## BAI_t3 3 28 9.00 7.73 6.00 8.29 4.45 0.00 29.00 29.00
## BAI_t4 4 32 6.78 8.53 4.50 4.85 5.19 0.00 33.00 33.00
## BAI_t5 5 28 7.93 8.56 5.00 6.75 5.93 0.00 37.00 37.00
## BAI_t6 6 21 7.14 7.84 3.00 5.76 2.97 0.00 26.00 26.00
## BSI_t1 7 33 51.55 23.77 56.00 52.63 26.69 4.00 86.00 82.00
## BSI_t2 8 30 31.70 20.21 31.50 29.33 20.76 1.00 91.00 90.00
## BSI_t3 9 28 28.25 15.78 27.50 28.29 22.24 4.00 53.00 49.00
## BSI_t4 10 32 22.00 20.80 17.50 18.96 15.57 1.00 101.00 100.00
## BSI_t5 11 28 23.79 19.26 21.00 21.88 17.79 0.00 75.00 75.00
## BSI_t6 12 22 19.91 15.61 18.50 18.28 13.34 1.00 63.00 62.00
## BDI_t1 13 33 20.94 10.60 21.00 20.63 11.86 1.00 42.00 41.00
## BDI_t2 14 30 12.87 10.12 11.00 11.88 11.86 0.00 36.00 36.00
## BDI_t3 15 28 11.04 6.68 10.50 10.75 7.41 0.00 28.00 28.00
## BDI_t4 16 32 8.22 7.71 6.00 7.12 5.93 0.00 35.00 35.00
## BDI_t5 17 28 8.29 7.10 8.50 7.54 7.41 0.00 26.00 26.00
## BDI_t6 18 22 5.59 6.70 3.00 4.56 4.45 0.00 26.00 26.00
## n_sessions 19 33 24.76 6.01 26.00 25.59 2.97 9.00 31.00 22.00
## age_th 20 32 32.16 6.56 30.00 31.04 2.97 23.00 51.00 28.00
## age_pat_startth 21 33 29.36 10.65 26.00 27.30 5.93 20.00 68.00 48.00
## pom_pre 22 33 77.72 34.27 80.00 79.65 44.97 5.00 126.33 121.33
## pom_int1 23 30 48.26 30.78 43.67 45.21 31.88 3.00 138.33 135.33
## pom_int2 24 28 42.29 22.59 42.50 42.17 29.40 4.00 82.67 78.67
## pom_post 25 32 32.48 29.74 22.33 28.03 21.00 2.00 145.00 143.00
## pom_fu1 26 28 34.71 28.29 33.17 31.65 31.38 0.00 108.00 108.00
## pom_fu2 27 21 27.24 22.29 25.00 24.69 22.24 1.67 77.67 76.00
## pat_id* 28 33 39.88 15.84 44.00 41.37 14.83 1.00 60.00 59.00
## the_id* 29 33 19.97 7.67 21.00 20.48 8.90 3.00 31.00 28.00
## pat_sex* 30 33 1.61 0.50 2.00 1.63 0.00 1.00 2.00 1.00
## the_sex* 31 33 1.76 0.44 2.00 1.81 0.00 1.00 2.00 1.00
## EFT_all 32 33 1.95 0.38 1.88 1.93 0.45 1.30 2.70 1.39
## SR_all 33 33 1.57 0.34 1.50 1.54 0.31 1.00 2.42 1.42
## DB_all 34 33 1.94 0.38 1.83 1.92 0.33 1.28 2.83 1.56
## I_all 35 33 1.76 0.65 1.58 1.69 0.62 1.00 3.50 2.50
## B_all 36 33 2.15 0.29 2.16 2.13 0.23 1.42 2.93 1.51
## C_all 37 33 2.36 0.35 2.33 2.35 0.37 1.73 3.15 1.42
## PC_all 38 33 4.24 0.46 4.30 4.24 0.61 3.39 5.00 1.61
## PD_all 39 33 1.35 0.23 1.27 1.33 0.20 1.07 2.07 1.00
## CF_all 40 33 4.58 0.24 4.67 4.59 0.25 4.17 5.00 0.83
## PE_all 41 33 1.89 0.36 1.74 1.87 0.22 1.33 2.67 1.33
## skew kurtosis se
## BAI_t1 -0.20 -0.97 1.51
## BAI_t2 0.84 -0.48 1.68
## BAI_t3 1.04 -0.02 1.46
## BAI_t4 1.92 2.92 1.51
## BAI_t5 1.60 2.49 1.62
## BAI_t6 1.35 0.41 1.71
## BSI_t1 -0.39 -1.09 4.14
## BSI_t2 1.01 0.72 3.69
## BSI_t3 -0.01 -1.38 2.98
## BSI_t4 1.83 4.13 3.68
## BSI_t5 0.90 0.13 3.64
## BSI_t6 0.95 0.50 3.33
## BDI_t1 0.08 -0.94 1.85
## BDI_t2 0.70 -0.55 1.85
## BDI_t3 0.49 -0.51 1.26
## BDI_t4 1.56 2.61 1.36
## BDI_t5 0.78 0.01 1.34
## BDI_t6 1.41 1.47 1.43
## n_sessions -1.21 0.52 1.05
## age_th 1.65 2.21 1.16
## age_pat_startth 2.04 4.00 1.85
## pom_pre -0.40 -0.98 5.97
## pom_int1 0.85 0.38 5.62
## pom_int2 0.01 -1.25 4.27
## pom_post 1.84 4.12 5.26
## pom_fu1 0.94 0.27 5.35
## pom_fu2 0.81 -0.36 4.86
## pat_id* -0.74 -0.55 2.76
## the_id* -0.47 -0.83 1.34
## pat_sex* -0.41 -1.88 0.09
## the_sex* -1.15 -0.70 0.08
## EFT_all 0.28 -1.03 0.07
## SR_all 0.62 -0.16 0.06
## DB_all 0.37 -0.60 0.07
## I_all 0.90 0.11 0.11
## B_all 0.45 0.96 0.05
## C_all 0.28 -0.77 0.06
## PC_all 0.01 -1.37 0.08
## PD_all 1.01 0.61 0.04
## CF_all -0.19 -0.89 0.04
## PE_all 0.61 -0.67 0.06
# Amount of Therapists
# df2 %>% filter(!is.na(the_id)) %>% dplyr::count(the_id,the_sex, sort = TRUE)
(9/32)*100 #-> 31.03 % male therapists
## [1] 28.125
(23/32)*100 #-> 68.97 % female therapists
## [1] 71.875
Inter-Rater
Reliabilität
# Data
df_icc <- read.spss("n33_3raterinnenICC.sav", to.data.frame = T)
df_icc$raterin<-as.factor(df_icc$raterin)
df_icc_items <- df_icc %>% dplyr::select(M1:M69)
item_names <- colnames(df_icc_items)
# Loop for ICC calculations
df_icc_summary <- data.frame()
for (x in item_names) {
z <-df_icc %>% dplyr::select(pat_id, raterin, t, x) %>% spread(raterin, x) %>% dplyr::select("1", "2", "3")
z.icc <- icc(z, model = "twoway", type = "agreement", unit = "single")
a <- data.frame(x, round(z.icc$value,3), z.icc$lbound, z.icc$ubound)
names(a) <- c("Item", "ICC Value", "UG - KI", "OG - KI")
df_icc_summary <- rbind(df_icc_summary, a)
}
# Tabellenvorberitung
df_icc_summary_rounded<-df_icc_summary %>% mutate_at(vars(-Item), funs(round(., 2)))
df_icc_summary_rounded_zero<-pmax(df_icc_summary_rounded,0)
df_icc_summary_rounded_zero[is.na(df_icc_summary_rounded_zero)] <- 0
# Tabelle abspeichern
df_icc_summary_rounded_zero
## Item ICC Value UG - KI OG - KI
## 1 M1 0.74 0.57 0.86
## 2 M2 0.77 0.62 0.88
## 3 M3 0.87 0.77 0.93
## 4 M4 0.73 0.54 0.86
## 5 M5 0.47 0.24 0.68
## 6 M6 0.57 0.35 0.75
## 7 M7 0.33 0.10 0.57
## 8 M9 0.58 0.36 0.76
## 9 M10 0.00 0.00 0.00
## 10 M11 0.37 0.14 0.61
## 11 M12 0.38 0.14 0.61
## 12 M13 0.00 0.00 0.24
## 13 M15 0.94 0.89 0.97
## 14 M16 0.00 0.00 0.22
## 15 M17 0.72 0.55 0.85
## 16 M18 0.00 0.00 0.26
## 17 M20 0.31 0.08 0.55
## 18 M21 0.42 0.19 0.65
## 19 M22 0.32 0.09 0.56
## 20 M23 0.00 0.00 0.23
## 21 M24 0.11 0.00 0.38
## 22 M25 0.36 0.12 0.59
## 23 M27 0.36 0.13 0.59
## 24 M28 0.10 0.00 0.31
## 25 M29 0.42 0.19 0.65
## 26 M31 0.19 0.00 0.44
## 27 M33 0.51 0.28 0.71
## 28 M34 0.80 0.67 0.90
## 29 M35 0.56 0.34 0.75
## 30 M36 0.56 0.34 0.75
## 31 M37 0.12 0.00 0.37
## 32 M39 0.41 0.18 0.63
## 33 M40 0.26 0.04 0.51
## 34 M43 0.17 0.00 0.44
## 35 M44 0.58 0.37 0.76
## 36 M46 0.16 0.00 0.41
## 37 M47 0.84 0.72 0.92
## 38 M48 0.34 0.11 0.58
## 39 M49 0.55 0.33 0.74
## 40 M50 0.41 0.18 0.64
## 41 M51 0.39 0.16 0.62
## 42 M54 0.41 0.16 0.64
## 43 M56 0.23 0.00 0.49
## 44 M60 0.00 0.00 0.24
## 45 M61 0.57 0.36 0.75
## 46 M62 0.93 0.87 0.96
## 47 M63 0.10 0.00 0.34
## 48 M64 0.65 0.45 0.81
## 49 M65 0.29 0.07 0.53
## 50 M66 0.60 0.39 0.77
## 51 M67 0.52 0.30 0.72
## 52 M68 0.00 0.00 0.26
## 53 M69 0.00 0.00 0.00
write_xlsx(df_icc_summary_rounded_zero,"sumary_icc.xlsx")
Subskalen: Zeitlicher
Verlauf
# Data
item_ausprägung <- read.spss("N183_Fragestellung_sub.sav", to.data.frame = T)
item_ausprägung$Subskala <- as.factor(item_ausprägung$Index1)
item_ausprägung$Bedingung <- as.factor(item_ausprägung$bedingung)
item_ausprägung$Therapie_Drittel <- as.numeric(item_ausprägung$t)
item_ausprägung$Rating <- (item_ausprägung$item)
item_ausprägung$Subskala <- revalue(item_ausprägung$Subskala, c(
"PD " ="Psychodynamic",
"PE " = "Process-Experiential",
"I " = "Interpersonal",
"PC " = "Person-Centered",
"CF " = "Common Factors",
"B " = "Behavioral",
"C " = "Cognitive",
"DB " = "Dialectic-Behavioral",
"EFT" = "Emotion-Focused",
"SR " = "Sef-Regulation"))
# Plot
Abbildung_5<-ggplot(item_ausprägung) +
stat_summary(aes(x = Therapie_Drittel, y = Rating, group = paste0(Subskala,Bedingung), color = Subskala, linetype=Bedingung), fun=mean, geom="line") +
ylim(1,5) +
xlim(1,3)+
theme_classic()+
scale_x_continuous(breaks=c(1,2,3))+
scale_linetype_discrete(labels=c("1" = "+EFT", "2" = "+SR"))
Abbildung_5
ggsave("Abbildung_5.jpg", plot = Abbildung_5, scale = 1,
width = 15,
height = 12,
units = c("cm"),
dpi = 500,)
Items: Häufigkeit pro
Therapeut*in
# Data
df_hm_orig <- read.spss("N183_Fragestellung1.sav", to.data.frame = T)
df_hm <- df_hm_orig %>% dplyr::select(the_id,t,PD:EFT)
df_hm$Therapeutin<-as.factor(df_hm$the_id)
df_hm_m <- df_hm %>%
group_by(Therapeutin) %>%
summarise_at(vars(PD:EFT), list(mean))
df_hm_m_long <- gather(df_hm_m, Subskala, Rating, PD:EFT, factor_key=TRUE)
df_hm_m_long <- na.omit(df_hm_m_long)
levels(df_hm_m_long$Therapeutin) <- 1:40
# Plot: Heatmap
Heatmap_Subskala_Th <- ggplot(df_hm_m_long, aes(Subskala, Therapeutin, fill=Rating)) +
geom_tile() +
scale_fill_continuous(limits = c(1, 5), breaks = seq(1, 5, by = 1)) +
guides(fill = guide_colourbar(barwidth = 0.5,
barheight = 15))+
scale_x_discrete("Subskala", labels = c(
"PD" ="Psychodynamic",
"PE" = "Process-Experiential",
"I" = "Interpersonal",
"PC" = "Person-Centered",
"CF" = "Common Factors",
"B" = "Behavioral",
"C" = "Cognitive",
"DB" = "Dialectic-Behavioral",
"EFT" = "Emotion-Focused",
"SR" = "Sef-Regulation"))+
theme(axis.text.x = element_text(angle = 45, hjust = 1))
Heatmap_Subskala_Th
ggsave("Heatmap_Subskala_Th.jpg", plot = Heatmap_Subskala_Th, scale = 1,
width = 12,
height = 13,
units = c("cm"),
dpi = 500,)
Plot:
Durchschnittliche Frequenz und Intensität der angewandten Subskalen pro
Bedingung
# Data
data_r_analysen_ready <- data_fragestellung2
data_r_analysen_ready$condition <- as.factor(data_r_analysen_ready$bedingung)
data_r_analysen_ready_long <- gather(data_r_analysen_ready, Subskala, measurement, PD_all:EFT_all, factor_key=TRUE)
# Boxplot
Boxplot_Bedingungen_Subskalen <- ggplot(data_r_analysen_ready_long, aes(x=Subskala, y=measurement, fill=condition)) +
geom_boxplot() +
theme_apa() +
xlab("Theorie-(un)spezfisiche Interventionen") +
scale_x_discrete(labels=c("PD_all" = "Psychodynamic",
"PE_all" = "Process-Experiential",
"I_all" = "Interpersonal",
"PC_all" = "Person-Centered",
"CF_all" = "Common Factors",
"B_all" = "Behavioral",
"C_all" = "Cognitive",
"DB_all" = "Dialectic-Behavioral",
"EFT_all" = "Emotion-Focused",
"SR_all" = "Sef-Regulation"
)) +
ylab("Rating") +
scale_fill_discrete(name = "Bedingung", labels=c("1" = "+EFT",
"2" = "+SR")) +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
Boxplot_Bedingungen_Subskalen
ggsave("Subskala_Wirkfaktoren.jpg", plot = Boxplot_Bedingungen_Subskalen, scale = 1,
width = 15,
height = 10,
units = c("cm"),
dpi = 300,)
## Alternativ: Basic Violine Plot
Violineplot_Bedingungen_Subskalen <- ggplot(data_r_analysen_ready_long, aes(x=Subskala, y=measurement, fill=bedingung)) +
geom_violin(size=0.2) +
theme_apa() +
xlab("Theorie (un)-spezfisiche Interventionen") +
scale_x_discrete(labels=c("PD_all" = "Psychodynamic",
"PE_all" = "Process-Experiential",
"I_all" = "Interpersonal",
"PC_all" = "Person-Centered",
"CF_all" = "Common Factors",
"B_all" = "Behavioral",
"C_all" = "Cognitive",
"DB_all" = "Dialectic-Behavioural",
"SR_all" = "Sef-Regulation",
"EFT_all" = "Emotion-Focused")) +
ylab("Rating") +
scale_fill_discrete(name = "Bedingung", labels=c("1" = "+EFT",
"2" = "+SR")) +
theme(axis.text.x = element_text(angle = 0))+
coord_flip()
Violineplot_Bedingungen_Subskalen
Hierarchical Linear
Modeling
Data
# Wide to Long
df2_long <- select(df2, BAI_t1, BAI_t2, BAI_t3, BAI_t4, BAI_t5, BAI_t6,
BSI_t1, BSI_t2, BSI_t3, BSI_t4, BSI_t5, BSI_t6,
BDI_t1, BDI_t2, BDI_t3, BDI_t4, BDI_t5, BDI_t6,
n_sessions, age_th, age_pat_startth, pom_pre, pom_int1, pom_int2, pom_post, pom_fu1, pom_fu2, pat_id, the_id, pat_sex, the_sex, EFT_all, SR_all, DB_all, I_all, B_all, C_all, PC_all, PD_all, CF_all, PE_all)%>%
gather("Time","Pomvalue", pom_pre: pom_fu2 ) %>%
mutate(Time = replace(Time,Time=="pom_pre", "0")) %>%
mutate(Time = replace(Time,Time=="pom_int1", "1")) %>%
mutate(Time = replace(Time,Time=="pom_int2", "2")) %>%
mutate(Time = replace(Time,Time=="pom_post", "3")) %>%
mutate(Time = replace(Time,Time=="pom_fu1", "4"))%>%
mutate(Time = replace(Time,Time=="pom_fu2", "5"))%>%
mutate(Time = as.numeric(Time))
Prüfung der
Vorraussetzungen
Varianzhomogenität
(homoscedasticity)
leveneTest(df2_long$Pomvalue, df2_long$Time)
## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 5 2.1993 0.05425 .
## 313
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Normalverteilung
der Residuen
df2_long$zSR_all <- scale(df2_long$SR_all)
df2_long$zEFT_all <- scale(df2_long$EFT_all)
df2_long$zB_all <- scale(df2_long$B_all)
df2_long$zC_all <- scale(df2_long$C_all)
df2_long$zI_all <- scale(df2_long$I_all)
df2_long$zPC_all <- scale(df2_long$PC_all)
df2_long$zCF_all <- scale(df2_long$CF_all)
df2_long$zDB_all <- scale(df2_long$DB_all)
df2_long$zPD_all <- scale(df2_long$PD_all)
df2_long$zPE_all <- scale(df2_long$PE_all)
PlotQQ(df2_long$zSR_all)
PlotQQ(df2_long$zEFT_all)
PlotQQ(df2_long$zB_all)
PlotQQ(df2_long$zC_all)
PlotQQ(df2_long$zI_all)
PlotQQ(df2_long$zPC_all)
PlotQQ(df2_long$zCF_all)
PlotQQ(df2_long$zDB_all)
PlotQQ(df2_long$zPD_all)
PlotQQ(df2_long$zPE_all)
summary(Linear.model.test <- lm(Pomvalue ~ SR_all + EFT_all + B_all + I_all + C_all
+ PC_all + CF_all + DB_all + PD_all, PE_all, data = df2_long))
##
## Call:
## lm(formula = Pomvalue ~ SR_all + EFT_all + B_all + I_all + C_all +
## PC_all + CF_all + DB_all + PD_all, data = df2_long, subset = PE_all)
##
## Residuals:
## Min 1Q Median 3Q Max
## -8.838e-12 1.400e-15 1.400e-15 5.290e-14 1.593e-13
##
## Coefficients: (7 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.032e+01 1.186e-13 3.398e+14 <2e-16 ***
## SR_all 4.091e+01 1.725e-13 2.372e+14 <2e-16 ***
## EFT_all -1.961e+01 1.289e-13 -1.521e+14 <2e-16 ***
## B_all NA NA NA NA
## I_all NA NA NA NA
## C_all NA NA NA NA
## PC_all NA NA NA NA
## CF_all NA NA NA NA
## DB_all NA NA NA NA
## PD_all NA NA NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.659e-13 on 363 degrees of freedom
## Multiple R-squared: 1, Adjusted R-squared: 1
## F-statistic: 4.368e+28 on 2 and 363 DF, p-value: < 2.2e-16
ols_test_normality(Linear.model.test)
## -----------------------------------------------
## Test Statistic pvalue
## -----------------------------------------------
## Shapiro-Wilk 0.047 0.0000
## Kolmogorov-Smirnov 0.4945 0.0000
## Cramer-von Mises 122 0.0000
## Anderson-Darling 124.4669 0.0000
## -----------------------------------------------
plot(Linear.model.test)
HLM Modelle
Intercept-Only
Modell
intercept.only.model <- lmer(data = df2_long, Pomvalue ~ 1 + (1 | pat_id), REML = TRUE)
summary(intercept.only.model)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Pomvalue ~ 1 + (1 | pat_id)
## Data: df2_long
##
## REML criterion at convergence: 3087.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.5843 -0.6905 -0.2231 0.5518 3.0178
##
## Random effects:
## Groups Name Variance Std.Dev.
## pat_id (Intercept) 313.9 17.72
## Residual 765.4 27.67
## Number of obs: 319, groups: pat_id, 61
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 45.329 2.768 60.730 16.38 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
confint(intercept.only.model)
## 2.5 % 97.5 %
## .sig01 13.24199 22.68635
## .sigma 25.44700 30.22198
## (Intercept) 39.85829 50.78983
performance::r2(intercept.only.model)
## # R2 for Mixed Models
##
## Conditional R2: 0.291
## Marginal R2: 0.000
performance::icc(intercept.only.model)
## # Intraclass Correlation Coefficient
##
## Adjusted ICC: 0.291
## Conditional ICC: 0.291
# Explained Variance verglichen mit dem "null-model"
ranova(intercept.only.model)
## ANOVA-like table for random-effects: Single term deletions
##
## Model:
## Pomvalue ~ (1 | pat_id)
## npar logLik AIC LRT Df Pr(>Chisq)
## <none> 3 -1543.9 3093.7
## (1 | pat_id) 2 -1564.8 3133.7 41.928 1 9.468e-11 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Time-as-Only-Predictor Modell
time.only.model <- lmer(data = df2_long, Pomvalue ~ Time + (1 | pat_id), REML = TRUE)
summary(time.only.model)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Pomvalue ~ Time + (1 | pat_id)
## Data: df2_long
##
## REML criterion at convergence: 2978.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.0505 -0.5922 -0.1543 0.4917 3.6067
##
## Random effects:
## Groups Name Variance Std.Dev.
## pat_id (Intercept) 372.1 19.29
## Residual 503.6 22.44
## Number of obs: 319, groups: pat_id, 61
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 65.3933 3.2839 111.9413 19.91 <2e-16 ***
## Time -8.8135 0.7597 265.3743 -11.60 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## Time -0.528
confint(time.only.model)
## 2.5 % 97.5 %
## .sig01 15.21743 24.039741
## .sigma 20.59938 24.476104
## (Intercept) 58.94238 71.840174
## Time -10.30240 -7.318512
anova(intercept.only.model, time.only.model) # Explained Variance compared to intercept-only model
## Data: df2_long
## Models:
## intercept.only.model: Pomvalue ~ 1 + (1 | pat_id)
## time.only.model: Pomvalue ~ Time + (1 | pat_id)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## intercept.only.model 3 3097.6 3108.9 -1545.8 3091.6
## time.only.model 4 2991.3 3006.4 -1491.7 2983.3 108.28 1 < 2.2e-16
##
## intercept.only.model
## time.only.model ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
performance::r2(time.only.model)
## # R2 for Mixed Models
##
## Conditional R2: 0.541
## Marginal R2: 0.202
0.207/0.632 # f2 = Marginal intercept.only - marginal time.only/1 -
## [1] 0.3275316
performance::icc(time.only.model)
## # Intraclass Correlation Coefficient
##
## Adjusted ICC: 0.425
## Conditional ICC: 0.339
Time-as-Only-Predictor Modell: Random-Effects Modelle
time2.only.model <- lmer(data = df2_long, Pomvalue ~ Time + (Time | pat_id), REML = TRUE)
summary (time2.only.model)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Pomvalue ~ Time + (Time | pat_id)
## Data: df2_long
##
## REML criterion at convergence: 2964.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.0600 -0.5311 -0.1367 0.3866 3.7634
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## pat_id (Intercept) 653.3 25.559
## Time 26.2 5.119 -0.66
## Residual 413.4 20.332
## Number of obs: 319, groups: pat_id, 61
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 65.2064 3.8203 60.8709 17.068 < 2e-16 ***
## Time -8.7489 0.9648 58.1973 -9.068 9.94e-13 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## Time -0.685
confint(time2.only.model)
## 2.5 % 97.5 %
## .sig01 19.6782293 32.2240408
## .sig02 -0.8428782 -0.3444323
## .sig03 2.9960091 7.1163074
## .sigma 18.4884036 22.5031125
## (Intercept) 57.6463104 72.7366312
## Time -10.6518008 -6.8370639
anova(time.only.model, time2.only.model)
## Data: df2_long
## Models:
## time.only.model: Pomvalue ~ Time + (1 | pat_id)
## time2.only.model: Pomvalue ~ Time + (Time | pat_id)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## time.only.model 4 2991.3 3006.4 -1491.7 2983.3
## time2.only.model 6 2982.1 3004.7 -1485.0 2970.1 13.218 2 0.001348 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
performance::r2(time2.only.model)
## # R2 for Mixed Models
##
## Conditional R2: 0.624
## Marginal R2: 0.199
0.218 # 0.484
## [1] 0.218
1 - 0.702 # 0.298
## [1] 0.298
0.484/0.298# f2 = 0.31
## [1] 1.624161
performance::icc(time2.only.model) # adjusted --> ICC
## # Intraclass Correlation Coefficient
##
## Adjusted ICC: 0.531
## Conditional ICC: 0.425
# Random Effects of Therapists
th.model <- lmer(data = df2_long, Pomvalue ~ 1 + (Time | the_id/pat_id), REML = TRUE)
Theorie-Spezifische
Subskalen als Prädiktoren
df_subscales<- df2_long %>% dplyr::select(EFT_all:PE_all)
subscale_names <- colnames(df_subscales)
plot_list = list()
n = 0
for (i in subscale_names) {
n <- n+1
x <- c(str_glue("{i}*Time +","(Time|pat_id)"))
form <- reformulate(x,response="Pomvalue")
mx <- lmer(form, data = df2_long)
print("______________________________________________")
print("Subskala:")
print(i)
print(summary(mx))
}
## [1] "______________________________________________"
## [1] "Subskala:"
## [1] "EFT_all"
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: form
## Data: df2_long
##
## REML criterion at convergence: 2954.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.0651 -0.5268 -0.1312 0.3805 3.7394
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## pat_id (Intercept) 656.36 25.619
## Time 26.91 5.187 -0.66
## Residual 413.39 20.332
## Number of obs: 319, groups: pat_id, 61
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 48.626 19.419 59.267 2.504 0.015 *
## EFT_all 7.889 9.055 59.237 0.871 0.387
## Time -6.451 5.015 58.072 -1.286 0.203
## EFT_all:Time -1.094 2.329 57.672 -0.470 0.640
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) EFT_ll Time
## EFT_all -0.980
## Time -0.679 0.665
## EFT_all:Tim 0.668 -0.680 -0.981
## [1] "______________________________________________"
## [1] "Subskala:"
## [1] "SR_all"
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: form
## Data: df2_long
##
## REML criterion at convergence: 2952
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.0914 -0.5197 -0.1349 0.4054 3.7439
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## pat_id (Intercept) 665.82 25.804
## Time 26.06 5.105 -0.67
## Residual 413.13 20.326
## Number of obs: 319, groups: pat_id, 61
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 64.8627 19.4816 58.9784 3.329 0.0015 **
## SR_all 0.2428 12.3412 58.9338 0.020 0.9844
## Time -3.6839 4.7890 54.8439 -0.769 0.4450
## SR_all:Time -3.2844 3.0402 55.3224 -1.080 0.2847
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) SR_all Time
## SR_all -0.980
## Time -0.694 0.681
## SR_all:Time 0.680 -0.694 -0.980
## [1] "______________________________________________"
## [1] "Subskala:"
## [1] "DB_all"
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: form
## Data: df2_long
##
## REML criterion at convergence: 2954.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.0898 -0.5400 -0.1152 0.3828 3.7484
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## pat_id (Intercept) 663.94 25.767
## Time 26.62 5.159 -0.66
## Residual 413.59 20.337
## Number of obs: 319, groups: pat_id, 61
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 56.192 18.703 59.727 3.004 0.00388 **
## DB_all 4.824 9.806 59.555 0.492 0.62455
## Time -5.420 4.776 58.460 -1.135 0.26113
## DB_all:Time -1.774 2.490 58.191 -0.712 0.47914
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) DB_all Time
## DB_all -0.979
## Time -0.680 0.665
## DB_all:Time 0.669 -0.683 -0.979
## [1] "______________________________________________"
## [1] "Subskala:"
## [1] "I_all"
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: form
## Data: df2_long
##
## REML criterion at convergence: 2952.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.0559 -0.5293 -0.1321 0.4020 3.7608
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## pat_id (Intercept) 670.82 25.900
## Time 25.58 5.058 -0.70
## Residual 413.89 20.344
## Number of obs: 319, groups: pat_id, 61
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 66.965 12.216 59.907 5.482 8.87e-07 ***
## I_all -1.046 6.891 59.525 -0.152 0.880
## Time -4.773 3.011 58.438 -1.585 0.118
## I_all:Time -2.371 1.704 58.768 -1.392 0.169
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) I_all Time
## I_all -0.949
## Time -0.712 0.676
## I_all:Time 0.674 -0.711 -0.948
## [1] "______________________________________________"
## [1] "Subskala:"
## [1] "B_all"
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: form
## Data: df2_long
##
## REML criterion at convergence: 2951.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.0484 -0.5318 -0.1341 0.4229 3.7367
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## pat_id (Intercept) 644.09 25.379
## Time 24.73 4.973 -0.64
## Residual 413.40 20.332
## Number of obs: 319, groups: pat_id, 61
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 31.793 28.113 59.215 1.131 0.263
## B_all 15.807 13.173 59.097 1.200 0.235
## Time 1.917 6.896 53.820 0.278 0.782
## B_all:Time -5.046 3.229 53.823 -1.563 0.124
##
## Correlation of Fixed Effects:
## (Intr) B_all Time
## B_all -0.991
## Time -0.679 0.673
## B_all:Time 0.673 -0.680 -0.990
## [1] "______________________________________________"
## [1] "Subskala:"
## [1] "C_all"
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: form
## Data: df2_long
##
## REML criterion at convergence: 2949.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.0427 -0.5240 -0.1632 0.4328 3.6906
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## pat_id (Intercept) 631.97 25.139
## Time 22.74 4.768 -0.63
## Residual 412.25 20.304
## Number of obs: 319, groups: pat_id, 61
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 27.604 25.927 58.940 1.065 0.2914
## C_all 16.201 11.050 58.897 1.466 0.1479
## Time 5.132 6.196 52.569 0.828 0.4113
## C_all:Time -5.973 2.634 52.335 -2.268 0.0275 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) C_all Time
## C_all -0.989
## Time -0.673 0.666
## C_all:Time 0.667 -0.675 -0.989
## [1] "______________________________________________"
## [1] "Subskala:"
## [1] "PC_all"
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: form
## Data: df2_long
##
## REML criterion at convergence: 2951.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.0620 -0.5324 -0.0966 0.4283 3.7941
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## pat_id (Intercept) 646.43 25.425
## Time 24.32 4.932 -0.64
## Residual 411.02 20.274
## Number of obs: 319, groups: pat_id, 61
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 20.400 38.017 59.092 0.537 0.5935
## PC_all 10.550 8.914 59.008 1.184 0.2413
## Time 9.530 9.496 58.413 1.004 0.3197
## PC_all:Time -4.301 2.222 58.007 -1.935 0.0578 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) PC_all Time
## PC_all -0.995
## Time -0.672 0.668
## PC_all:Time 0.670 -0.673 -0.995
## [1] "______________________________________________"
## [1] "Subskala:"
## [1] "PD_all"
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: form
## Data: df2_long
##
## REML criterion at convergence: 2950.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.0559 -0.5306 -0.1229 0.3799 3.7537
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## pat_id (Intercept) 631.16 25.123
## Time 25.74 5.074 -0.64
## Residual 412.94 20.321
## Number of obs: 319, groups: pat_id, 61
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 30.345 21.838 59.788 1.390 0.170
## PD_all 25.292 15.602 59.784 1.621 0.110
## Time -1.480 5.490 56.743 -0.270 0.788
## PD_all:Time -5.274 3.917 56.688 -1.346 0.184
##
## Correlation of Fixed Effects:
## (Intr) PD_all Time
## PD_all -0.985
## Time -0.680 0.670
## PD_all:Time 0.671 -0.680 -0.985
## [1] "______________________________________________"
## [1] "Subskala:"
## [1] "CF_all"
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: form
## Data: df2_long
##
## REML criterion at convergence: 2953.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.0507 -0.5310 -0.1336 0.3934 3.7476
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## pat_id (Intercept) 665.89 25.805
## Time 27.26 5.221 -0.66
## Residual 413.24 20.328
## Number of obs: 319, groups: pat_id, 61
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 93.6225 68.9246 60.0119 1.358 0.179
## CF_all -6.2142 15.0443 60.0325 -0.413 0.681
## Time -10.3072 17.5708 58.0372 -0.587 0.560
## CF_all:Time 0.3425 3.8272 57.8985 0.089 0.929
##
## Correlation of Fixed Effects:
## (Intr) CF_all Time
## CF_all -0.998
## Time -0.684 0.683
## CF_all:Time 0.684 -0.685 -0.998
## [1] "______________________________________________"
## [1] "Subskala:"
## [1] "PE_all"
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: form
## Data: df2_long
##
## REML criterion at convergence: 2954.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.0582 -0.5192 -0.1200 0.3934 3.7169
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## pat_id (Intercept) 657.10 25.634
## Time 27.29 5.224 -0.66
## Residual 412.62 20.313
## Number of obs: 319, groups: pat_id, 61
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 49.7500 18.5289 59.0771 2.685 0.0094 **
## PE_all 7.5234 8.8193 59.0955 0.853 0.3971
## Time -8.2171 4.7841 58.1874 -1.718 0.0912 .
## PE_all:Time -0.2643 2.2709 58.0848 -0.116 0.9077
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) PE_all Time
## PE_all -0.978
## Time -0.682 0.667
## PE_all:Time 0.669 -0.683 -0.979
